Overview

Dataset statistics

Number of variables23
Number of observations7716
Missing cells17248
Missing cells (%)9.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory184.0 B

Variable types

CAT15
NUM8

Warnings

centro_escolar_acceso has a high cardinality: 143 distinct values High cardinality
fecha_nacimiento has a high cardinality: 4557 distinct values High cardinality
provincia has a high cardinality: 52 distinct values High cardinality
municipio has a high cardinality: 545 distinct values High cardinality
des_subacesso is highly correlated with des_accesoHigh correlation
des_acceso is highly correlated with des_subacessoHigh correlation
tipo_traslado has 7294 (94.5%) missing values Missing
nota_acceso has 2275 (29.5%) missing values Missing
nota_admision_def has 3878 (50.3%) missing values Missing
centro_escolar_acceso has 3211 (41.6%) missing values Missing
cod_provincia has 144 (1.9%) missing values Missing
provincia has 144 (1.9%) missing values Missing
cod_municipio has 151 (2.0%) missing values Missing
municipio has 151 (2.0%) missing values Missing
nota_acceso is highly skewed (γ1 = 42.41238095) Skewed
fecha_nacimiento is uniformly distributed Uniform

Reproduction

Analysis started2021-05-14 09:07:22.778988
Analysis finished2021-05-14 09:07:30.912735
Duration8.13 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

expediente
Real number (ℝ≥0)

Distinct3542
Distinct (%)45.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1385.427164
Minimum0
Maximum4548
Zeros2
Zeros (%)< 0.1%
Memory size60.3 KiB
2021-05-14T11:07:30.964575image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile64
Q1347
median1007
Q32053
95-th percentile3824.25
Maximum4548
Range4548
Interquartile range (IQR)1706

Descriptive statistics

Standard deviation1220.830672
Coefficient of variation (CV)0.8811944098
Kurtosis-0.345117593
Mean1385.427164
Median Absolute Deviation (MAD)782
Skewness0.854126773
Sum10689956
Variance1490427.531
MonotocityNot monotonic
2021-05-14T11:07:31.075910image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
119100.1%
 
91100.1%
 
79100.1%
 
11590.1%
 
12490.1%
 
12190.1%
 
13990.1%
 
1890.1%
 
3690.1%
 
13290.1%
 
Other values (3532)762398.8%
 
ValueCountFrequency (%) 
02< 0.1%
 
150.1%
 
240.1%
 
350.1%
 
460.1%
 
ValueCountFrequency (%) 
45481< 0.1%
 
45471< 0.1%
 
45461< 0.1%
 
45451< 0.1%
 
45441< 0.1%
 

cod_plan
Real number (ℝ≥0)

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean717.5861846
Minimum715
Maximum725
Zeros0
Zeros (%)0.0%
Memory size60.3 KiB
2021-05-14T11:07:31.148632image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum715
5-th percentile715
Q1716
median717
Q3718
95-th percentile724
Maximum725
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.489841637
Coefficient of variation (CV)0.003469745782
Kurtosis1.569648606
Mean717.5861846
Median Absolute Deviation (MAD)1
Skewness1.551897577
Sum5536895
Variance6.199311377
MonotocityIncreasing
2021-05-14T11:07:31.212865image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%) 
717231730.0%
 
716176822.9%
 
718128116.6%
 
715104713.6%
 
7244075.3%
 
7232923.8%
 
7191792.3%
 
7221381.8%
 
7211241.6%
 
725941.2%
 
ValueCountFrequency (%) 
715104713.6%
 
716176822.9%
 
717231730.0%
 
718128116.6%
 
7191792.3%
 
ValueCountFrequency (%) 
725941.2%
 
7244075.3%
 
7232923.8%
 
7221381.8%
 
7211241.6%
 

des_plan
Categorical

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size60.3 KiB
GRADO EN EDUCACIÓN PRIMARIA (CÁCERES)
2317 
GRADO EN EDUCACIÓN INFANTIL (CÁCERES)
1768 
MÁSTER UNIVERSITARIO EN FORMACIÓN DEL PROFESORADO EN EDUCACIÓN SECUNDARIA
1281 
GRADO EN EDUCACIÓN SOCIAL
1047 
GRADO EN EDUCACIÓN PRIMARIA. Modalidad Bilingüe (Español-Inglés)
407 
Other values (6)
896 
ValueCountFrequency (%) 
GRADO EN EDUCACIÓN PRIMARIA (CÁCERES)231730.0%
 
GRADO EN EDUCACIÓN INFANTIL (CÁCERES)176822.9%
 
MÁSTER UNIVERSITARIO EN FORMACIÓN DEL PROFESORADO EN EDUCACIÓN SECUNDARIA128116.6%
 
GRADO EN EDUCACIÓN SOCIAL104713.6%
 
GRADO EN EDUCACIÓN PRIMARIA. Modalidad Bilingüe (Español-Inglés)4075.3%
 
MÁSTER UNIVERSITARIO EN INVESTIGACIÓN EN CIENCIAS SOCIALES2923.8%
 
MÁSTER UNIVERSITARIO EN INVESTIGACIÓN EN CIENCIAS SOCIALES Y JURÍDICAS1792.3%
 
MÁSTER UNIVERSITARIO EN EDUCACIÓN DIGITAL1381.8%
 
MÁSTER UNIVERSITARIO EN ANTROPOLOGÍA SOCIAL1241.6%
 
MÁSTER UNIV. DE ENSEÑANZA PORTUGUÉS LENG. EXTRANJERA PARA HISPANOHABLANTES941.2%
 
2021-05-14T11:07:31.277593image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-05-14T11:07:31.345376image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length75
Median length37
Mean length45.29134266
Min length25
Distinct13
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size60.3 KiB
2013-14
764 
2011-12
715 
2019-20
685 
2020-21
676 
2017-18
629 
Other values (8)
4247 
ValueCountFrequency (%) 
2013-147649.9%
 
2011-127159.3%
 
2019-206858.9%
 
2020-216768.8%
 
2017-186298.2%
 
2010-116298.2%
 
2014-156268.1%
 
2016-176218.0%
 
2012-136168.0%
 
2009-105927.7%
 
Other values (3)116315.1%
 
2021-05-14T11:07:31.421842image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2021-05-14T11:07:31.492023image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length7
Mean length7
Min length7

exp_cerrado
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size60.3 KiB
S
4464 
N
3252 
ValueCountFrequency (%) 
S446457.9%
 
N325242.1%
 
2021-05-14T11:07:31.554390image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-05-14T11:07:31.597350image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:31.640862image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

exp_trasladado
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size60.3 KiB
N
7294 
S
 
422
ValueCountFrequency (%) 
N729494.5%
 
S4225.5%
 
2021-05-14T11:07:31.704500image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-05-14T11:07:31.748249image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:31.790891image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

tipo_traslado
Categorical

MISSING

Distinct3
Distinct (%)0.7%
Missing7294
Missing (%)94.5%
Memory size60.3 KiB
I
277 
E
96 
S
49 
ValueCountFrequency (%) 
I2773.6%
 
E961.2%
 
S490.6%
 
(Missing)729494.5%
 
2021-05-14T11:07:31.853003image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-05-14T11:07:31.898257image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:31.953252image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length2.8906169
Min length1

exp_bloqueado
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size60.3 KiB
N
7607 
S
 
109
ValueCountFrequency (%) 
N760798.6%
 
S1091.4%
 
2021-05-14T11:07:32.023811image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-05-14T11:07:32.068547image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:32.113447image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1
Distinct39
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size60.3 KiB
2010-11
757 
2009-10
694 
2008-09
602 
2012-13
591 
2015-16
581 
Other values (34)
4491 
ValueCountFrequency (%) 
2010-117579.8%
 
2009-106949.0%
 
2008-096027.8%
 
2012-135917.7%
 
2015-165817.5%
 
2016-175677.3%
 
2013-145657.3%
 
2018-195457.1%
 
2014-155447.1%
 
2017-185427.0%
 
Other values (29)172822.4%
 
2021-05-14T11:07:32.195182image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique4 ?
Unique (%)0.1%
2021-05-14T11:07:32.274993image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length7
Mean length7
Min length7
Distinct15
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size60.3 KiB
JUN
5617 
SEP
918 
EXT
 
492
JUL
 
372
DIC
 
103
Other values (10)
 
214
ValueCountFrequency (%) 
JUN561772.8%
 
SEP91811.9%
 
EXT4926.4%
 
JUL3724.8%
 
DIC1031.3%
 
FEB1011.3%
 
OCT360.5%
 
ENE330.4%
 
NOV300.4%
 
FEX40.1%
 
Other values (5)100.1%
 
2021-05-14T11:07:32.344646image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)< 0.1%
2021-05-14T11:07:32.494869image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

acceso
Real number (ℝ≥0)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.554561949
Minimum1
Maximum17
Zeros0
Zeros (%)0.0%
Memory size60.3 KiB
2021-05-14T11:07:32.548912image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q35
95-th percentile5
Maximum17
Range16
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9809864
Coefficient of variation (CV)0.7754700962
Kurtosis4.381976968
Mean2.554561949
Median Absolute Deviation (MAD)0
Skewness1.318247331
Sum19711
Variance3.924307119
MonotocityNot monotonic
2021-05-14T11:07:32.601111image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
1436856.6%
 
5238230.9%
 
384711.0%
 
4670.9%
 
10360.5%
 
17150.2%
 
91< 0.1%
 
ValueCountFrequency (%) 
1436856.6%
 
384711.0%
 
4670.9%
 
5238230.9%
 
91< 0.1%
 
ValueCountFrequency (%) 
17150.2%
 
10360.5%
 
91< 0.1%
 
5238230.9%
 
4670.9%
 

des_acceso
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size60.3 KiB
Selectividad
4368 
Título Universitario
2382 
Formación Profesional
847 
Mayores de 25/40/45 años
 
67
Traslado de Expediente (Estudios Españoles)
 
36
Other values (2)
 
16
ValueCountFrequency (%) 
Selectividad436856.6%
 
Título Universitario238230.9%
 
Formación Profesional84711.0%
 
Mayores de 25/40/45 años670.9%
 
Traslado de Expediente (Estudios Españoles)360.5%
 
Bachillerato Sin Prueba de Acceso150.2%
 
Acc. Unión Europea/Acuerdos Internac. reciprocidad1< 0.1%
 
2021-05-14T11:07:32.670511image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2021-05-14T11:07:32.718500image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:32.798814image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length50
Median length12
Mean length15.75220321
Min length12

sub_acceso
Real number (ℝ≥0)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.170943494
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Memory size60.3 KiB
2021-05-14T11:07:32.854096image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.150028275
Coefficient of variation (CV)0.6780405515
Kurtosis-1.862286064
Mean3.170943494
Median Absolute Deviation (MAD)1
Skewness0.08540710816
Sum24467
Variance4.622621585
MonotocityNot monotonic
2021-05-14T11:07:32.912293image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
1356246.2%
 
5251232.6%
 
6124416.1%
 
23554.6%
 
4420.5%
 
31< 0.1%
 
ValueCountFrequency (%) 
1356246.2%
 
23554.6%
 
31< 0.1%
 
4420.5%
 
5251232.6%
 
ValueCountFrequency (%) 
6124416.1%
 
5251232.6%
 
4420.5%
 
31< 0.1%
 
23554.6%
 

des_subacesso
Categorical

HIGH CORRELATION

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size60.3 KiB
LOE (Grados)
2512 
Titulado universitario
2229 
Bachillerato LOMCE
1244 
Ciclos formativos
712 
LOGSE
558 
Other values (13)
461 
ValueCountFrequency (%) 
LOE (Grados)251232.6%
 
Titulado universitario222928.9%
 
Bachillerato LOMCE124416.1%
 
Ciclos formativos7129.2%
 
LOGSE5587.2%
 
Curso de Adaptación1532.0%
 
Formación Profesional II991.3%
 
COU520.7%
 
P.A.U. MAYORES DE 25 (Grados)460.6%
 
Técnico Deportivo Superior360.5%
 
Other values (8)751.0%
 
2021-05-14T11:07:32.980994image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)< 0.1%
2021-05-14T11:07:33.045418image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length45
Median length18
Mean length16.41861068
Min length3

nota_acceso
Real number (ℝ≥0)

MISSING
SKEWED

Distinct1936
Distinct (%)35.6%
Missing2275
Missing (%)29.5%
Infinite0
Infinite (%)0.0%
Mean10.31085297
Minimum1.369
Maximum7078
Zeros0
Zeros (%)0.0%
Memory size60.3 KiB
2021-05-14T11:07:33.113273image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1.369
5-th percentile5.23
Q15.77
median6.322
Q37.052
95-th percentile8.26
Maximum7078
Range7076.631
Interquartile range (IQR)1.282

Descriptive statistics

Standard deviation150.3889885
Coefficient of variation (CV)14.58550413
Kurtosis1823.998362
Mean10.31085297
Median Absolute Deviation (MAD)0.612
Skewness42.41238095
Sum56101.351
Variance22616.84788
MonotocityNot monotonic
2021-05-14T11:07:33.197535image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
7360.5%
 
6340.4%
 
6.5260.3%
 
8260.3%
 
5.75200.3%
 
6.29190.2%
 
5.5180.2%
 
5.45180.2%
 
6.27160.2%
 
6.2160.2%
 
Other values (1926)521267.5%
 
(Missing)227529.5%
 
ValueCountFrequency (%) 
1.3691< 0.1%
 
1.5271< 0.1%
 
1.5341< 0.1%
 
1.6371< 0.1%
 
1.6481< 0.1%
 
ValueCountFrequency (%) 
70781< 0.1%
 
60901< 0.1%
 
59161< 0.1%
 
7111< 0.1%
 
6431< 0.1%
 

nota_admision_def
Real number (ℝ≥0)

MISSING

Distinct2112
Distinct (%)55.0%
Missing3878
Missing (%)50.3%
Infinite0
Infinite (%)0.0%
Mean7.599868161
Minimum5
Maximum13.514
Zeros0
Zeros (%)0.0%
Memory size60.3 KiB
2021-05-14T11:07:33.283452image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5.4717
Q16.4585
median7.434
Q38.49375
95-th percentile10.45
Maximum13.514
Range8.514
Interquartile range (IQR)2.03525

Descriptive statistics

Standard deviation1.502297615
Coefficient of variation (CV)0.1976741679
Kurtosis0.3679885192
Mean7.599868161
Median Absolute Deviation (MAD)1.01
Skewness0.6809711428
Sum29168.294
Variance2.256898123
MonotocityNot monotonic
2021-05-14T11:07:33.390707image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
7230.3%
 
8200.3%
 
6170.2%
 
6.5140.2%
 
7.6120.2%
 
6.82120.2%
 
6.64110.1%
 
6.73100.1%
 
7.07100.1%
 
7.64100.1%
 
Other values (2102)369947.9%
 
(Missing)387850.3%
 
ValueCountFrequency (%) 
53< 0.1%
 
5.0042< 0.1%
 
5.0082< 0.1%
 
5.012< 0.1%
 
5.0141< 0.1%
 
ValueCountFrequency (%) 
13.5141< 0.1%
 
13.4061< 0.1%
 
13.2541< 0.1%
 
13.2321< 0.1%
 
13.2141< 0.1%
 

centro_escolar_acceso
Categorical

HIGH CARDINALITY
MISSING

Distinct143
Distinct (%)3.2%
Missing3211
Missing (%)41.6%
Memory size60.3 KiB
205-I.E.S. AL-QÁZERES
 
289
231-I.E.S. NORBA CAESARINA
 
267
230-I.E.S. EL BROCENSE
 
251
220-I.E.S. UNIVERSIDAD LABORAL
 
116
170-I.E.S. SANTA EULALIA
 
102
Other values (138)
3480 
ValueCountFrequency (%) 
205-I.E.S. AL-QÁZERES2893.7%
 
231-I.E.S. NORBA CAESARINA2673.5%
 
230-I.E.S. EL BROCENSE2513.3%
 
220-I.E.S. UNIVERSIDAD LABORAL1161.5%
 
170-I.E.S. SANTA EULALIA1021.3%
 
203-I.E.S. ALAGÓN901.2%
 
240-I.E.S. ÁGORA831.1%
 
226-I.E.S. GABRIEL Y GALÁN(Plasen)801.0%
 
235-SAGRADO CORAZÓN DE JESÚS (Cá)700.9%
 
206-I.E.S. SAN PEDRO DE ALCÁNTARA690.9%
 
Other values (133)308840.0%
 
(Missing)321141.6%
 
2021-05-14T11:07:33.493949image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique9 ?
Unique (%)0.2%
2021-05-14T11:07:33.595901image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length44
Median length21
Mean length16.97809746
Min length3

sexo
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size60.3 KiB
D
5322 
H
2394 
ValueCountFrequency (%) 
D532269.0%
 
H239431.0%
 
2021-05-14T11:07:33.661274image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-05-14T11:07:33.703015image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:33.748336image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

fecha_nacimiento
Categorical

HIGH CARDINALITY
UNIFORM

Distinct4557
Distinct (%)59.1%
Missing0
Missing (%)0.0%
Memory size60.3 KiB
1991-04-09
 
9
1993-10-22
 
7
1991-12-09
 
7
1991-12-13
 
7
1993-05-10
 
7
Other values (4552)
7679 
ValueCountFrequency (%) 
1991-04-0990.1%
 
1993-10-2270.1%
 
1991-12-0970.1%
 
1991-12-1370.1%
 
1993-05-1070.1%
 
1992-02-0670.1%
 
1996-12-1370.1%
 
1993-04-2770.1%
 
1992-10-0970.1%
 
1997-02-2070.1%
 
Other values (4547)764499.1%
 
2021-05-14T11:07:33.837988image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2653 ?
Unique (%)34.4%
2021-05-14T11:07:34.005130image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length10
Min length10

cod_provincia
Real number (ℝ≥0)

MISSING

Distinct52
Distinct (%)0.7%
Missing144
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean10.19202324
Minimum0
Maximum60
Zeros2
Zeros (%)< 0.1%
Memory size60.3 KiB
2021-05-14T11:07:34.080488image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q16
median10
Q310
95-th percentile28
Maximum60
Range60
Interquartile range (IQR)4

Descriptive statistics

Standard deviation7.265960922
Coefficient of variation (CV)0.7129066279
Kurtosis12.57603149
Mean10.19202324
Median Absolute Deviation (MAD)0
Skewness3.465561699
Sum77174
Variance52.79418811
MonotocityNot monotonic
2021-05-14T11:07:34.165611image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
10398851.7%
 
6282836.7%
 
281792.3%
 
8590.8%
 
45550.7%
 
41510.7%
 
37450.6%
 
14320.4%
 
11310.4%
 
48290.4%
 
Other values (42)2753.6%
 
(Missing)1441.9%
 
ValueCountFrequency (%) 
02< 0.1%
 
1120.2%
 
21< 0.1%
 
3130.2%
 
43< 0.1%
 
ValueCountFrequency (%) 
601< 0.1%
 
521< 0.1%
 
511< 0.1%
 
5070.1%
 
4950.1%
 

provincia
Categorical

HIGH CARDINALITY
MISSING

Distinct52
Distinct (%)0.7%
Missing144
Missing (%)1.9%
Memory size60.3 KiB
CÁCERES
3988 
BADAJOZ
2828 
MADRID
 
179
BARCELONA
 
59
TOLEDO
 
55
Other values (47)
463 
ValueCountFrequency (%) 
CÁCERES398851.7%
 
BADAJOZ282836.7%
 
MADRID1792.3%
 
BARCELONA590.8%
 
TOLEDO550.7%
 
SEVILLA510.7%
 
SALAMANCA450.6%
 
CÓRDOBA320.4%
 
CÁDIZ310.4%
 
BIZKAIA290.4%
 
Other values (42)2753.6%
 
(Missing)1441.9%
 
2021-05-14T11:07:34.268368image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique8 ?
Unique (%)0.1%
2021-05-14T11:07:34.356748image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length22
Median length7
Mean length6.958786936
Min length3

cod_municipio
Real number (ℝ≥0)

MISSING

Distinct323
Distinct (%)4.3%
Missing151
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean259.3964309
Minimum1
Maximum996
Zeros0
Zeros (%)0.0%
Memory size60.3 KiB
2021-05-14T11:07:34.436985image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median215
Q3480
95-th percentile760
Maximum996
Range995
Interquartile range (IQR)479

Descriptive statistics

Standard deviation267.9584479
Coefficient of variation (CV)1.033007459
Kurtosis-1.115392323
Mean259.3964309
Median Absolute Deviation (MAD)214
Skewness0.525997892
Sum1962334
Variance71801.72979
MonotocityNot monotonic
2021-05-14T11:07:34.520426image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1298838.7%
 
4104856.3%
 
5844415.7%
 
2152683.5%
 
5161962.5%
 
7601652.1%
 
2641572.0%
 
55801.0%
 
785761.0%
 
740620.8%
 
Other values (313)264734.3%
 
(Missing)1512.0%
 
ValueCountFrequency (%) 
1298838.7%
 
81< 0.1%
 
10230.3%
 
121< 0.1%
 
1550.1%
 
ValueCountFrequency (%) 
9961< 0.1%
 
9301< 0.1%
 
9281< 0.1%
 
9091< 0.1%
 
9061< 0.1%
 

municipio
Categorical

HIGH CARDINALITY
MISSING

Distinct545
Distinct (%)7.2%
Missing151
Missing (%)2.0%
Memory size60.3 KiB
CÁCERES
2117 
MÉRIDA
484 
BADAJOZ
463 
PLASENCIA
441 
DON BENITO
 
268
Other values (540)
3792 
ValueCountFrequency (%) 
CÁCERES211727.4%
 
MÉRIDA4846.3%
 
BADAJOZ4636.0%
 
PLASENCIA4415.7%
 
DON BENITO2683.5%
 
NAVALMORAL DE LA MATA1952.5%
 
VILLANUEVA DE LA SERENA1642.1%
 
CORIA1572.0%
 
MADRID1341.7%
 
ALMENDRALEJO801.0%
 
Other values (535)306239.7%
 
(Missing)1512.0%
 
2021-05-14T11:07:34.612352image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique219 ?
Unique (%)2.9%
2021-05-14T11:07:34.698170image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length27
Median length7
Mean length10.00790565
Min length3

Interactions

2021-05-14T11:07:25.000663image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:25.088467image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:25.153373image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:25.224745image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:25.290172image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:25.356082image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:25.418513image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:25.484287image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:25.548747image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:25.613725image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:25.678474image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:25.830150image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:25.900210image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:25.987332image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:26.055355image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:26.126266image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:26.195232image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:26.274954image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:26.375789image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:26.511251image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:26.640862image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:26.725560image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:26.805488image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:26.887901image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:26.990341image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:27.099788image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:27.207800image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:27.303102image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:27.371473image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:27.440745image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:27.507413image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:27.580294image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:27.662619image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:27.730833image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:27.799045image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:27.874284image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:27.943362image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:28.012927image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:28.079629image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:28.149864image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:28.218026image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:28.362545image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:28.425910image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:28.496109image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:28.560627image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:28.625063image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:28.686198image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:28.751077image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:28.814107image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:28.881269image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:28.948879image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:29.022816image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:29.091417image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:29.167444image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:29.238776image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:29.307261image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:29.372855image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:29.437290image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:29.502487image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:29.575449image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:29.644002image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:29.710811image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:29.774456image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:29.841819image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2021-05-14T11:07:34.760243image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-14T11:07:34.870709image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-14T11:07:34.977355image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-14T11:07:35.098910image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-05-14T11:07:35.259781image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-05-14T11:07:30.014622image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:30.357378image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:30.534592image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-05-14T11:07:30.759546image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Sample

First rows

expedientecod_plandes_plananio_apertura_expedienteexp_cerradoexp_trasladadotipo_trasladoexp_bloqueadoanio_convocatoria_accesoconvocatoria_accesoaccesodes_accesosub_accesodes_subacessonota_accesonota_admision_defcentro_escolar_accesosexofecha_nacimientocod_provinciaprovinciacod_municipiomunicipio
04715GRADO EN EDUCACIÓN SOCIAL2009-10SNNaNN2007-08JUN1Selectividad1LOGSE7.12NaNNaND1990-05-266.0BADAJOZ410.0MÉRIDA
15715GRADO EN EDUCACIÓN SOCIAL2009-10SNNaNN2008-09JUN3Formación Profesional1Ciclos formativos8.71NaNNaND1985-10-316.0BADAJOZ410.0MÉRIDA
26715GRADO EN EDUCACIÓN SOCIAL2009-10SNNaNN2008-09JUN3Formación Profesional1Ciclos formativos9.14NaNNaND1989-12-136.0BADAJOZ390.0MANCHITA
37715GRADO EN EDUCACIÓN SOCIAL2009-10SNNaNN2008-09JUN3Formación Profesional1Ciclos formativos8.29NaNNaND1985-10-316.0BADAJOZ410.0MÉRIDA
48715GRADO EN EDUCACIÓN SOCIAL2009-10SNNaNN2008-09JUN1Selectividad1LOGSE7.13NaNNaND1991-11-126.0BADAJOZ265.0FUENTE DEL MAESTRE
59715GRADO EN EDUCACIÓN SOCIAL2009-10SNNaNN2008-09JUN1Selectividad1LOGSE8.17NaNNaND1991-03-126.0BADAJOZ265.0FUENTE DEL MAESTRE
610715GRADO EN EDUCACIÓN SOCIAL2009-10SNNaNN2008-09JUN1Selectividad1LOGSE7.66NaNNaND1991-05-136.0BADAJOZ265.0FUENTE DEL MAESTRE
712715GRADO EN EDUCACIÓN SOCIAL2009-10SNNaNN2008-09JUN3Formación Profesional1Ciclos formativos6.50NaNNaND1981-10-0410.0CÁCERES1.0CÁCERES
813715GRADO EN EDUCACIÓN SOCIAL2009-10SNNaNN2008-09JUN3Formación Profesional1Ciclos formativos7.43NaNNaND1989-01-106.0BADAJOZ410.0MÉRIDA
915715GRADO EN EDUCACIÓN SOCIAL2009-10SNNaNN2008-09JUN1Selectividad1LOGSE7.65NaNNaND1991-05-296.0BADAJOZ410.0MÉRIDA

Last rows

expedientecod_plandes_plananio_apertura_expedienteexp_cerradoexp_trasladadotipo_trasladoexp_bloqueadoanio_convocatoria_accesoconvocatoria_accesoaccesodes_accesosub_accesodes_subacessonota_accesonota_admision_defcentro_escolar_accesosexofecha_nacimientocod_provinciaprovinciacod_municipiomunicipio
7706136725MÁSTER UNIV. DE ENSEÑANZA PORTUGUÉS LENG. EXTRANJERA PARA HISPANOHABLANTES2020-21NNNaNN2009-10JUN5Título Universitario1Titulado universitarioNaNNaNNaNH1988-07-126.0BADAJOZ95.0BIENVENIDA
7707137725MÁSTER UNIV. DE ENSEÑANZA PORTUGUÉS LENG. EXTRANJERA PARA HISPANOHABLANTES2020-21NNNaNN2019-20JUN5Título Universitario1Titulado universitarioNaNNaNNaND1992-07-0321.0HUELVA430.0LEPE
7708139725MÁSTER UNIV. DE ENSEÑANZA PORTUGUÉS LENG. EXTRANJERA PARA HISPANOHABLANTES2020-21NNNaNN2019-20JUN5Título Universitario1Titulado universitarioNaNNaNNaNH1984-11-2823.0JAÉN1.0JAÉN
7709141725MÁSTER UNIV. DE ENSEÑANZA PORTUGUÉS LENG. EXTRANJERA PARA HISPANOHABLANTES2020-21NNNaNN2017-18JUN5Título Universitario1Titulado universitarioNaNNaNNaND1994-06-066.0BADAJOZ1.0BADAJOZ
7710142725MÁSTER UNIV. DE ENSEÑANZA PORTUGUÉS LENG. EXTRANJERA PARA HISPANOHABLANTES2020-21NNNaNN2019-20JUN5Título Universitario1Titulado universitarioNaNNaNNaND1997-11-253.0ALICANTE650.0TORREVIEJA
7711144725MÁSTER UNIV. DE ENSEÑANZA PORTUGUÉS LENG. EXTRANJERA PARA HISPANOHABLANTES2020-21NNNaNN2019-20JUN5Título Universitario1Titulado universitarioNaNNaNNaND1991-12-2715.0A CORUÑA1.0A CORUÑA
7712145725MÁSTER UNIV. DE ENSEÑANZA PORTUGUÉS LENG. EXTRANJERA PARA HISPANOHABLANTES2020-21NNNaNN2019-20JUN5Título Universitario1Titulado universitarioNaNNaNNaND1975-01-16NaNNaNNaNNaN
7713146725MÁSTER UNIV. DE ENSEÑANZA PORTUGUÉS LENG. EXTRANJERA PARA HISPANOHABLANTES2020-21NNNaNN2019-20JUN5Título Universitario1Titulado universitarioNaNNaNNaND1986-09-2249.0ZAMORA222.0FUENTESAÚCO
7714148725MÁSTER UNIV. DE ENSEÑANZA PORTUGUÉS LENG. EXTRANJERA PARA HISPANOHABLANTES2020-21NNNaNN2015-16JUL5Título Universitario1Titulado universitarioNaNNaNNaND1994-05-1710.0CÁCERES1.0CÁCERES
7715149725MÁSTER UNIV. DE ENSEÑANZA PORTUGUÉS LENG. EXTRANJERA PARA HISPANOHABLANTES2020-21NNNaNN2016-17NOV5Título Universitario1Titulado universitarioNaNNaNNaND1994-05-016.0BADAJOZ1.0BADAJOZ